Systems and Methods for Image Processing to Determine Blood Flow

ABSTRACT

Systems and methods are disclosed for assessing a risk of disease. One method includes obtaining an anatomic model associated with a target anatomy; modeling, using a processor, an injection of one or more virtual contrast agents into the anatomic model; performing a simulation of flow of blood and the one or more virtual contrast agents through the anatomic model; and computing one or more characteristics of concentration associated with the one or more virtual contrast agents at one or more locations in the anatomic model based on the simulation.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.61/982,580 filed Apr. 22, 2014, the entire disclosure of which is herebyincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical modeling and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods formodeling of blood flow rate using virtual contrast agents to computemetrics indicating functional significance of stenoses.

BACKGROUND

Coronary artery disease may cause the blood vessels providing blood tothe heart to develop lesions, such as a stenosis (abnormal narrowing ofa blood vessel). As a result, blood flow to the heart may be restricted.A patient suffering from coronary artery disease may experience chestpain, referred to as chronic stable angina during physical exertion orunstable angina when the patient is at rest. A more severe manifestationof disease may lead to myocardial infarction, or heart attack.

A need exists to provide more accurate data relating to coronarylesions, e.g., size, shape, location, functional significance (e.g.,whether the lesion impacts blood flow), etc. Patients suffering fromchest pain and/or exhibiting symptoms of coronary artery disease may besubjected to one or more tests that may provide some indirect evidencerelating to coronary lesions. For example, noninvasive tests may includeelectrocardiograms, biomarker evaluation from blood tests, treadmilltests, echocardiography, single positron emission computed tomography(SPECT), and positron emission tomography (PET). These noninvasivetests, however, typically do not provide a direct assessment of coronarylesions or assess blood flow rates. The noninvasive tests may provideindirect evidence of coronary lesions by looking for changes inelectrical activity of the heart (e.g., using electrocardiography(ECG)), motion of the myocardium (e.g., using stress echocardiography),perfusion of the myocardium (e.g., using PET or SPECT), or metabolicchanges (e.g., using biomarkers).

For example, anatomic data may be obtained noninvasively using coronarycomputed tomographic angiography (CCTA). CCTA may be used for imaging ofpatients with chest pain and involves using computed tomography (CT)technology to image the heart and the coronary arteries following anintravenous infusion of a contrast agent. However, CCTA also cannotprovide direct information on the functional significance of coronarylesions, e.g., whether the lesions affect blood flow. In addition, sinceCCTA is purely a diagnostic test, it can neither be used to predictchanges in coronary blood flow, pressure, or myocardial perfusion underother physiologic states (e.g., exercise), nor can it be used to predictoutcomes of interventions.

Thus, patients may require an invasive test, such as diagnostic cardiaccatheterization, to visualize coronary lesions. Diagnostic cardiaccatheterization may include performing conventional coronary angiography(CCA) to gather anatomic data on coronary lesions by providing a doctorwith an image of the size and shape of the arteries. CCA, however, doesnot provide data for assessing the functional significance of coronarylesions. For example, a doctor may not be able to diagnose whether acoronary lesion is harmful without determining whether the lesion isfunctionally significant. Thus, CCA has led to a procedure referred toas an “oculostenotic reflex”, in which interventional cardiologistsinsert a stent for every lesion found with CCA regardless of whether thelesion is functionally significant. As a result, CCA may lead tounnecessary operations on the patient, which may pose added risks topatients and may result in unnecessary heath care costs for patients.

During diagnostic cardiac catheterization, the functional significanceof a coronary lesion may be assessed invasively by measuring thefractional flow reserve (FFR) of an observed lesion. FFR is defined asthe ratio of the mean blood pressure downstream of a lesion divided bythe mean blood pressure upstream from the lesion, e.g., the aorticpressure, under conditions of increased coronary blood flow, e.g., wheninduced by intravenous administration of adenosine. Blood pressures maybe measured by inserting a pressure wire into the patient. Thus, thedecision to treat a lesion based on the determined FFR may be made afterthe initial cost and risk of diagnostic cardiac catheterization hasalready been incurred.

To reduce the above disadvantages of invasive FFR measurements, methodshave been developed for assessing coronary anatomy, myocardialperfusion, and coronary artery flow noninvasively. Specifically,computational fluid dynamics (CFD) simulations have been successfullyused to predict spatial and temporal variations of flow rate andpressure of blood in arteries, including FFR. Such methods and systemsbenefit cardiologists who diagnose and plan treatments for patients withsuspected coronary artery disease, and predict coronary artery flow andmyocardial perfusion under conditions that cannot be directly measured,e.g., exercise, and to predict outcomes of medical, interventional, andsurgical treatments on coronary artery blood flow and myocardialperfusion.

However, correlation between calculated functional significance ofstenoses and conclusions given by experimental data may be improved.Therefore, a need exists to improve reliability of measurements forindicating functional significance of stenoses. More specifically, aneed exists to improve measurements based on flow rates as means fordetermining functional significance of stenoses.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for assessing a risk of heart disease. One methodincludes: obtaining an anatomic model associated with a target anatomy;modeling, using a processor, an injection of one or more virtualcontrast agents into the anatomic model; performing a simulation of flowof blood and the one or more virtual contrast agents through theanatomic model; and computing one or more characteristics ofconcentration associated with the one or more virtual contrast agents atone or more locations in the anatomic model based on the simulation.

In accordance with another embodiment, a system for assessing a risk ofheart disease comprises: a data storage device storing instructions forassessing risk of heart disease; and a processor configured for:obtaining an anatomic model associated with a target anatomy; modeling,using a processor, an injection of one or more virtual contrast agentsinto the anatomic model; performing a simulation of flow of blood andthe one or more virtual contrast agents through the anatomic model; andcomputing one or more characteristics of concentration associated withthe one or more virtual contrast agents at one or more locations in theanatomic model based on the simulation.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for assessing a risk ofheart disease is provided. The method includes: obtaining an anatomicmodel associated with a target anatomy; modeling, using a processor, aninjection of one or more virtual contrast agents into the anatomicmodel; performing a simulation of flow of blood and the one or morevirtual contrast agents through the anatomic model; and computing one ormore characteristics of concentration associated with the one or morevirtual contrast agents at one or more locations in the anatomic modelbased on the simulation.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forassessing risk of disease, according to an exemplary embodiment of thepresent disclosure.

FIG. 2A is a block diagram of an exemplary method of assessing risk ofdisease using a simulated virtual contrast agent flow simulation,according to an exemplary embodiment of the present disclosure.

FIG. 2B is a block diagram of an exemplary method of correcting TAGscores using CCO, according to an exemplary embodiment of the presentdisclosure.

FIG. 2C is a block diagram of an exemplary method of calculating CCO,according to an exemplary embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary method of enriching ahemodynamic metric with one or more measurements from the virtualcontrast agent simulation to assign diagnoses, according to an exemplaryembodiment of the present disclosure.

FIG. 4 is a block diagram of an exemplary method for assessing aseverity of a stenosis based on a TAG score calculated using a bloodflow simulation of a virtual contrast agent, according to an exemplaryembodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

CFD simulations have been successfully used to predict spatial andtemporal variations of flow rate and pressure of blood in arteries,including FFR. Alternatives to FFR may include evaluating flow rates inorder to infer the functional significance of stenoses. The alternativesmay serve to replace, verify, compliment, and/or supplement conclusionsbased on FFR. Flow rate metrics that may measure functional significanceof stenosis include, for example, transluminal attenuation gradient(TAG), corrected thrombosis in myocardial infarction frame count (CTFC),thrombolysis in myocardial infarction myocardial perfusion grade (TMPG),and corrected coronary opacification (CCO). These exemplary metrics maybe developed to evaluate severity of stenoses, either as standalonemetrics, or as compliments to each other and/or to other measurements(e.g., FFR). Metrics may compliment another, e.g., where a TAG scoresupplements a finding in FFR_(CT) or FFR_(CT) is consistent with afinding based on a TAG score. Essentially, the metrics present multipleways to assess likelihood of heart disease. Therefore, one metric mayoffer an assessment, and using multiple measures or scores mayreinforce, verify, and/or clarify the assessment.

TAG, CTFC, TMPG, and CCO involve analyzing flow rates, meaningexperimental data is derived from images of contrast agents travelingthrough blood vessels. Using TAG, CTFC, TMPG, and/or CCO to calculatefunctional severity may be based on the notion that blood flow velocitymay increase at a stenosis. Due to the increased velocity, contrastagent released in the blood stream may be washed away faster downstreamof a stenosis. Contrast agent washing away quickly due to highervelocity near a stenosis may be connected to a low luminal intensity andtherefore, a low attenuation gradient, low frame count, or greatercorrected coronary opacification differences. Abnormal flow may alsocontribute to lower TIMI myocardial perfusion grades (TMPG). In thisway, TAG, CTFC, TMPG, and/or CCO may be related to stenosis severity.However, limitations in image acquisition in relation to contrast agentflow rates translate to limitations in reliability of experimental TAGscores, CTFC, TMPG, and CCO in providing assessments on severity ofstenosis. Therefore, a need exists for improving measurements of TAGscores, CTFC, TMPG, and CCO. The following disclosure is directed toemploying blood flow simulations with virtual contrast agent(s) in orderto improve TAG score, CTFC, TMPG, and CCO analysis, thereby permittingevaluation of functional significance of stenoses based on TAG, CTFC,TMPG and/or CCO data. The following discussion describes each of themetrics TAG, CTFC, TMPG, and CCO in more detail.

TAG is sometimes characterized as the slope of a linear regression fitbetween luminal intensity and axial distance. In other words, TAG may bethe rate of decrease in luminal intensity per unit distance. Asdiscussed above, TAG may have the potential to serve as an indication offunctional severity of stenoses. TAG may be computed by calculatingand/or analyzing contrast concentration along an artery of interest andmeasuring a gradient in the region of interest along the artery. In thisway, a TAG score may be inversely proportional to severity of astenosis. A lower TAG score may indicate a higher degree of stenosis(due to higher velocity blood flow near the stenosis), while a higherTAG score may indicate a low degree or absence of stenosis (due tonormal or expected blood flow rate near the stenosis). TAG score may beadded to coronary CTA to improve diagnostic accuracy, especially invessels with calcified lesions.

TAG scores may be measured directly from computed tomography (CT) scans.For example, a TAG score may typically be computed using Hounsfieldunits calculated across lengths of 5mm or 10 mm. For instance, a64-slice coronary computed tomography angiograph (cCTA) may be used tomeasure radio-density across stenosis in 5 mm length increments, wherethe difference in radio intensities across measurements may be reportedas TAG scores. One study reported that in a cohort of 54 patients, a TAGcutoff of −15 HU/10 mm may predict FFR<=0.8 with a sensitivity of 77%,specificity of 74%, positive predictive value of 67%, and negativepredictive value of 86% (“Transluminal attenuation gradient in coronarycomputed tomography angiography may be a novel noninvasive approach tothe identification of functionally significant coronary artery stenosis:a comparison with fractional flow reserve,” JACC, 2013). However,another study showed that compared to FFR, sensitivity of TAG scores maybe 38%, with an overall accuracy of 67% (“Noninvasive diagnosis ofischemic-causing coronary stenosis using CT angiography: diagnosticvalue of transluminal attenuation gradient and fractional flow reservecomputed from coronary CT angiography compared to invasively measuredfractional flow reserve,” JACC: cardiovascular imaging, 2012). In otherwords, while TAG may help evaluate functional severity of stenoses,usage of TAG is still being assessed. Thus, TAG score thresholds are notyet perceived as a common metric, for example, for helping triagepatients who are candidates for stenting.

Concerns regarding TAG score are related to (i) insufficient contrastmaterial, especially in distal stenoses, for TAG score analysis to beuseful and/or (ii) sufficiency of TAG scores as a standalone metric.Regarding insufficient contrast, while TAG scores measured directly fromCT scans may be assumed to be reliable in vessels with good flow rateand sufficient contrast material, the dependence of TAG score on luminalintensity means that distal vessels or vessels with low flow may beprone to substantial errors in direct estimation of TAG score from CTscans. Therefore, a need exists to compensate for limitations relatingto TAG scores, especially in relation to distal vessels or vessels withlow flow.

In one embodiment, computational prediction of TAG scores may be used toimprove accuracy in TAG scores. For example, blood flow simulations mayprovide theoretical TAG scores, e.g., for distal vessels. In oneembodiment, the simulations may include a simulation of virtual contrastagent flowing through a patient-specific model. Advection-diffusionequations for simulated conditions may then yield computationallypredicted TAG scores. For instance, advection-diffusion equations may beused to calculate concentration of contrast agent in the arteries ofinterest. The gradient in concentration profile along lumen centerlinesmay have one-to-one correspondence with TAG scores. Advection-diffusionequations may include a partial differential equation describingtransport of particles in a fluid domain. A variable to solve for in thepartial differential equation may include concentration of contrastagent, where velocity of the fluid domain may be calculated by solvingthe Navier-Stokes equations. Boundary conditions for the partialdifferential equations may be the contrast concentration at time t=0(e.g., where the concentration may be based on the amount and locationof contrast injection) and conditions at the boundary of thecomputational domain (where gradient of concentration may be zero).Alternately, arterial walls may also be assumed to be a continuous sinksince a network of microvessels may be modeled based on contrast data inproximal vessels with good flow-rate. Diffusivity of contrast agent inthe fluid domain may be assumed as a known variable. Alternately, thediffusivity may be calculated using contrast concentration at specificknown points and solving an inverse problem.

To improve the use of a TAG score as a standalone metric, improvementsto accuracy and versatility of TAG metrics (e.g., using CFD analysis ofvirtual contrast agent(s)) may permit TAG to operate as a standalonemetric. In the interim or in addition, TAG may be used in combinationwith other hemodynamic parameters to infer functional severity ofdisease. For example, TAG may be used to compliment CFD simulations andimprove the accuracy and/or interpretation of FFR_(CT) (e.g., ascalculated in U.S. Pat. No. 8,315,812 filed Jan. 25, 2011, the entiredisclosure of which is hereby incorporated by reference herein in itsentirety). FFR_(CT) may sometimes fall in an indeterminate zone (e.g.,between 0.75 to 0.85 or between 0.7 to 0.9). FFR_(CT) values greaterthan 0.9 or 0.85 indicate a non-significant stenosis, while values below0.75 or 0.7 may indicate a functionally significant stenosis. However,if a FFR_(CT) value is between 0.75-0.85 or 0.7-0.9, it may be unknownin some cases whether a lesion is functionally significant. Therefore, adesire exists to improve diagnostic evaluation of FFT_(CT) in theindeterminate zone.

As previously discussed, TAG scores may be calculated based onsimulations, for instance, by solving an advection-diffusion equation ofvirtually simulated contrast agent flow. Using the calculatedsimulations, a TAG threshold score may be determined and assigned basedon a score that optimally predicts functionally significant lesions. Forexample, a comparison of simulated TAG scores to FFR may provide insightinto threshold TAG scores associated with various levels of functionalseverity of stenoses. Therefore, in vessels where FFR_(CT) is in anindeterminate zone, a TAG score may be evaluated relative to a TAGthreshold score. Depending on the comparison, an assessment may be madeas to whether a disease is functionally significant. For example, ifFFR_(CT) is 0.82, functional significance of the stenosis by FFT_(CT),alone, may be indeterminate. However, if FFR_(CT) is 0.82 and a TAGthreshold score is −15 Hounsfield units/10 mm, a TAG score of −25Hounsfield units/10 mm may prompt the inference that a vessel is,indeed, diseased. In one embodiment, a machine learning algorithm maymap FFT_(CT), TAG scores, flow rates, and other features used to measureFFR. Based on the algorithm, a hybrid FFR_(CT-TAG) value may becalculated, where the FFR_(CT-TAG) value may have a higher diagnosticaccuracy than FFT_(CT) (consistent with U.S. application Ser. No.13/895,893 filed May 16, 2013, the entire disclosure of which is herebyincorporated by reference herein in its entirety).

Regarding CTFC, CTFC may refer to the number of (imaging) frames passedfor contrast agent dye to attenuate to a certain degree orconcentration, or for the contrast agent to reach standardized landmarksin portions of vessels, distal from a point of contrast agent injection.The time elapsed for the contrast agent to attenuate or reach thelandmarks may serve as indication of functional significance ofstenoses. As discussed previously, blood flow velocity may increase inthe area of a stenosis, so timing based on CTFC is related to flow rateof a contrast agent, and consequently, severity of a stenosis.Embodiments relating to CTFC may also be applied to other Thrombolysisin Myocardial Infarction (TIMI) derivative measures (e.g., TIMIMyocardial Perfusion Grade (TMPG)).

TMPG is a measure of flow through the myocardium (i.e., myocardialperfusion). While TAG, CTFC, and CCO may relate to coronary artery flow,analysis of TMPG may be derived from measuring myocardial perfusion,e.g., by observing a contrast agent passing through myocardialcapillaries. On a coronary angiogram, for example, the contrast agentmay be observed, such that imaged myocardium may appear with “blush”indicating the flow of the contrast agent. The TMPG may quantify this“blush.” For example, TMPG includes scores 0-3, with 0 being a failureof contrast agent perfusion (e.g., no or minimal blush). A TMPG of 3indicates normal perfusion, where there is a “blush” appearance in themyocardium and washout of the dye, as expected, after three cardiaccycles. TMPGs less than 3 may indicate abnormal or problematic flow.

CCO is a measure typically used to normalize flow measurements. CCO maybe calculated as the quotient of coronary segment intraluminalHounsfield value (HU) divided by the intraluminal HU taken at thedescending aorta. In some cases, the HU is the mean HU, and HU is basedon images taken in the same axial plane for both the coronary segmentand the descending aorta. In some embodiments, using this quotient mayhelp normalize, for example, TAG scores and CTFC, since TAG and CTFCdata is susceptible to transluminal attenuation attributable to imaging(e.g., gating), rather than flow. CCO may correct for transluminal HUlost due to the imaging process. Meanwhile, a CCO difference may alsoserve as a standalone metric for evaluating severity of stenoses. A CCOdifference may be calculated by subtracting a CCO measured from alocation proximal a stenosis, from a CCO measured at a location distalto a stenosis. In other words, the CCO difference may be a CCOpost-stenosis, subtracted from CCO pre-stenosis. The CCO difference maybe higher for vessels with significant stenoses, than vessels withinsignificant stenoses. As described before, this may be becausecontrast agent may wash out more quickly where a stenosis issignificant, thus leading to a larger difference in radiodensity (e.g.,HU) between CCO pre-stenosis and CCO post-stenosis.

In summary, while TAG scores, CTFC, TMPG, and CCO have the potential tononinvasively indicate functional severity of stenosis, the reliabilityof these metrics for predicting severity of stenosis may be improved.Therefore, the present disclosure is directed to a method forcomputationally calculating TAG scores, CTFC, TMPG, and/or CCO usingvirtual contrast agent flow simulations. Furthermore, the presentdisclosure is directed to a method for determining ranges or particularscores for comparing or confirming conclusions drawn from differentmetrics. For example, the present disclosure is directed to a method ofdetermining a TAG threshold score such that calculated TAG scores may beused to complement hemodynamic parameters (e.g., FFR) to better evaluatefunctional significance of stenoses.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for using virtual contrast agentconcentrations and CFD to compute functional significance of stenoses.Specifically, FIG. 1 depicts a plurality of physicians 102 and thirdparty providers 104, any of whom may be connected to an electronicnetwork 100, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. Physicians 102 and/or thirdparty providers 104 may create or otherwise obtain images of one or morepatients' cardiac and/or vascular systems. The physicians 102 and/orthird party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, etc. Physicians 102 and/or third partyproviders 104 may transmit the cardiac/vascular images and/orpatient-specific information to server systems 106 over the electronicnetwork 100. Server systems 106 may include storage devices for storingimages and data received from physicians 102 and/or third partyproviders 104. Server systems 106 may also include processing devicesfor processing images and data stored in the storage devices.

FIG. 2A is a block diagram of an exemplary method 200 of assessing riskof disease using a simulated virtual contrast agent flow simulation,according to an exemplary embodiment. In some instances, method 200 maybe used for assessing risk of heart disease. In one embodiment, step 201may include acquiring a digital representation of a system. For example,step 201 may include acquiring a digital scan encompassing a biologicalor other fluid system that is to be studied. The digital scan orrepresentation may include an image-based representation, measuredvariables, a list or table of parameter values and featurerepresentative of the system, or a combination thereof. Therepresentation and accompanying data may be loaded from an electronicstorage device (e.g., hard drive, RAM, network drive, etc.) into acomputational device (e.g., computer, laptop, etc.) used to perform eachof the following steps. In one embodiment, step 201 may further includeisolating a system of interest. For example, a system of interest may beisolated by delineating a geometry, system properties, and/or specificconditions (e.g., a section of a vessel, geometric parameters associatedwith the section, and/or a hyperemic state). This aspect of step 201 mayencompass additional steps, for example, steps for image processing andreconstructing the system from a raw, received image (e.g., the digitalrepresentation of the system of interest) such as in U.S. Pat. No.8,315,812 which is incorporated by reference.

Step 203 may include performing blood flow simulations using virtualcontrast agents in the arteries of interest. In one instance, step 203may include using clinical variables and a reconstructed image from step201 to assign lumped parameter boundary conditions that model resistanceof micro-vessels. Then, step 203 of performing simulations may includesolving Navier-Stokes equations to evaluate pressure and velocitiesthroughout the computational model. Step 203 may further includepost-processing the simulation. For example, post-processing may be usedto calculate variables for predicting disease. In one such scenario,variables may be compared to reference pressures and flow rates fordisease predictions. In some cases, post-processing may involveaggregating and integrating blood pressures and flow-rates along vesselsof interest. Disease may be calculated by comparing the aggregated orintegrated blood pressures and/or flow rates to reference values, e.g.,aortic pressure considered to be normal or healthy, and where the ratioof local to aortic blood pressure yields FFR. Post-processing may alsoinvolve mapping a metric on the surfaces of the reconstructed model andoutputting a resulting graphical figure.

Step 205 may include calculating contrast concentration. For example,contrast concentration may be determined by solving anadvection-diffusion equation. In one embodiment, step 205 may includeassigning initial conditions. For example, step 205 may includeassigning a known value of virtual contrast concentration near a sourcelocation, where contrast is typically injected prior to imaging. Theamount of virtual contrast may be patient-specific or a value based onan average from a population of patients. To model diffusivity forcalculating contrast concentration, step 205 may include understandingand determining properties of diffusivity through blood flow. Forexample, for a two-phase system of contrast agent in blood, diffusivitymay depend on density of two mediums (e.g., the contrast agent andblood), as well as viscosity of blood. While an amount of virtualcontrast may be inferred from a population-based average amount,diffusivity may only be patient-specific. Properties of blood, includingdiffusivity (and by extension, viscosity), may change based onaggregation of red blood cells. For example, red blood cells may displayunique mechanical properties, in which the cells may clump (e.g.,aggregate). Tendencies in patients' blood for red blood cell aggregationmay be associated with blood sheer rate, which may correspond toviscosity of blood. Using an inverse problem, diffusivity may beinferred directly from contrast concentration in proximal vessels withgood flow.

In one embodiment, solving an advection diffusion equation for contrastconcentration may include using velocities (e.g., in the form ofvelocity fields) calculated in step 203. For instance, applying velocitydata and initial conditions to advection diffusion equations may yieldcontrast concentration by way of advection of virtual contrast agent astime progresses.

In one embodiment, step 207 may include calculating (CFD-derived) TAGscores, CTFC, TMPG, or a combination thereof. For example, step 207 mayinclude using contrast agent concentration across lumen centerlines tocalculate local gradients. The local gradients may then be mapped toHounsfield units/mm by multiplying the gradients by a constant. In oneembodiment, step 207 may further include accounting for correctedcoronary opacification (CCO) in calculating TAG and/or CTFC scores.Further description of this calculation is provided in FIG. 2B.

In one embodiment, step 209 may include assigning a TAG threshold scoreand/or threshold CTFC, TMPG, or CCO difference. For example, step 209may be based on a database of patient information associated with adisease or risk of a disease. In some cases, determining an optimal TAGthreshold score may include using a least squares error metric toidentify patients at risk of a disease. Some scenarios may involve atraining database and/or a training TAG score calculating algorithm thatmay dynamically determine and/or adjust a TAG threshold score accordingto collected patient information.

FIG. 2B is a block diagram of an exemplary method 220 of correcting TAGscores using CCO, according to an exemplary embodiment. While CCO may beused to correct TAG scores in this exemplary embodiment, anyflow-related metric that may be determined from a flow simulation (e.g.,a virtual contrast agent flow simulation) may be used to correct,supplement, or verify a risk assessment. While some assessments mayfocus on either CTFC, TMPG, TAG, or CCO, other assessments may employvarious combinations of the metrics. Method 220 is an exemplaryembodiment, focusing on a combination including TAG and CCO.

Imaging of the heart often includes acquiring images throughout orduring multiple cardiac cycles. Therefore, in addition to typicalvariability between separate images, factors caused by cardiac cycles(e.g., timing, cardiac output, bolus geometry, etc.) may contribute tocontrast attenuation between various acquired images of coronaryarteries. CCO may calculate variations in contrast attenuation caused bydifferent cardiac cycles. Since TAG scores may be based on contrastattenuation (e.g., decrease in luminal intensity per unit distance),taking into account CCO may help to normalize TAG scores. In someinstances, failing to normalize contrast attenuation (e.g., using CCO)may result in data where worsening stenosis does not necessarily displaya relationship with contrast attenuation. Therefore, step 207 of method200 may include and/or prompt method 220 in order to strengthenassociations between TAG and functional severity of stenoses.

In one embodiment, CCO applies to each “slice” or image in a scan, whereCCO may be calculated as,

CCO=coronary artery HU/aorta(HU).

Furthermore, a difference in CCO across stenoses may be calculated,wherein a CCO difference may be calculated as,

CCO difference=pre-stenosis CCO−post-stenosis CCO.

In one embodiment, step 221 may include receiving a system of interest(e.g., from step 201) and calculated contrast concentrations from asimulation (e.g., from step 205). Step 223 may include identifying oneor more stenoses within the system of interest. Step 225 may includedetermining, for each stenoses, a pre-stenosis CCO and a post-stenosisCCO. For example, step 225 may include defining pre-stenosis andpost-stenosis CCO to be the minimum CCO (CCO_(min)) at locationspre-stenosis and post-stenosis, respectively. Step 225 may furtherinclude calculating and/or receiving CCO values (e.g., CCO_(min)). Step227 may include calculating CCO differences, for example, CCOdifferences across each of the determined stenoses.

Once CCO differences are calculated, the CCO differences may be used invarious ways. In one embodiment, CCO differences may be used to evaluatecoronary blood flow. For instance, CCO differences across one or morestenoses may be analyzed collectively to make inferences on blood flowthrough the modeled system of interest. FIG. 2C provides further detailof such an application of CCO differences. In another embodiment, CCOdifference for each stenosis may provide an indication of a severity ofa coronary stenosis. In addition, CCO difference may be combined withanother metric to evaluate severity of a coronary stenosis. For example,step 229 may include outputting CCO calculations (from step 225) and/orCCO difference calculations (from step 227) to an entity calculating TAGscores (e.g., an entity performing method 200, and more specifically,step 207). Alternately, step 229 may include identifying a metric andcorrecting or normalizing the metric based on the CCO and/or CCOdifferences. In other words, CCO may be calculated using computationalmeans to normalize or improve TAG scores derived from imaging. Further,the normalized and/or improved TAG scores may be used to refine FFR_(CT)analyses.

FIG. 2C is a block diagram of an exemplary method 240 of calculatingCCO, according to an exemplary embodiment. In some embodiments, CCO mayestimate coronary blood flow, independent of TAG. Steps 241-245 may besimilar to steps 201-205, since these steps provide the data from whichflow rate simulations (and consequently, evaluation of stenoses) may bederived.

In one embodiment, step 241 may include acquiring a digital scan and/orreconstruction of a system of interest. For example, the data may beloaded from an electronic storage device into a computational device.Step 241 may further include delineating a specific geometry, a set ofsystem properties, and/or specific conditions for an analysis. Step 243may include performing blood flow simulations, for instance, throughparticular arteries of interest. This step may include evaluatingpressure and velocities throughout a computational model made based onthe geometries of the representation(s) provided in step 241. Step 245may include calculating virtual contrast concentration for flow throughthe model based on the simulations. In one embodiment, step 247 mayinclude identifying coronary stenoses within the model, where step 249may include calculating CCO differences across the stenoses. In oneembodiment, mean CCO may be known to approach 1.0 (e.g.,CCO=0.979±0.070) for “normal” arteries with normal blood flow. This CCOmay be denoted as, an “expected CCO.” Step 251 may include comparingcalculated CCO with expected CCO and/or determining whether calculatedCCO deviates from expected CCO. Step 253 may include outputting adetermination of abnormal or normal resting coronary flow, based on thecomparison of calculated CCO versus “normal” CCO.

FIG. 3 is a block diagram of an exemplary method 300 of enrichinghemodynamic metrics (e.g., FFR_(CT)) using measurements from the virtualcontrast agent simulation in order to refine or improve diagnoses,according to an exemplary embodiment. In one embodiment, step 301 mayinclude determining a primary metric for diagnosis. For example, aprimary metric may include a disease-specific hemodynamic metric (e.g.,FFR_(CT)). Other instances of primary metrics may include coronary flowreserve or coronary flow velocity reserve, for example. As previouslydiscussed, some embodiments may include TAG as a “primary” metric andanother hemodynamic metric as secondary or supplemental. For instance,TAG may be a primary metric, and FFR_(CT) a supplemental metric.Essentially, the metrics all assess likelihood of heart disease. Usingmultiple measures or scores serves to reinforce and/or verifyassessments.

In some embodiments, step 301 may include selecting a primary metricfrom a collection of metrics for diagnosis. The selection may be basedon the disease, patient information, and/or averaged patient populationinformation, etc. Step 303 may include causing a determination ofwhether the hemodynamic metric may reliably identify a disease and/ordistinguish a disease from another disease. If the hemodynamic metric isinsufficient to determine a disease, step 305 may be prompted, wherestep 305 may include evaluating a TAG score, CTFC, TMPG, and/or CCO. Forexample, step 305 may include comparing a TAG score, CTFC, TMPG, and/orCCO with a threshold TAG score, CTFC, TMPG, and/or CCO, respectively.Step 305 may include employing the metrics, TAG, CTFC, TMPG, and CCOindividually, all together, or in any combination. Step 307 may includeinferring and/or determining that a vessel of interest is diseased,based on the comparison of step 305 (e.g., whether the TAG score exceedsor falls below the threshold TAG score) and the determined primarymetric. The determination of step 307 may be based on informationregarding blood particles' flow along arteries, along with hemodynamicvariables. In some instances, the determination in step 307 may includea probability that the vessel is diseased or a probability of thelikelihood of the diagnosis being correct. In one embodiment, step 309may include outputting the diagnosis probability to an electronicstorage medium (e.g., a hard disk, RAM, network drive, user display,etc.).

FIG. 4 is a block diagram of an exemplary method 400 of a specificembodiment for assessing severity of a stenosis based on a TAG scorecalculated using a blood flow simulation with a virtual contrast agent,according to an exemplary embodiment. In other words, method 400 is aspecific embodiment employing method 200 and 300 of determining a TAGscore, and then using the TAG score to assess risk of heart disease. Inone embodiment, step 401 may include acquiring and processing inputdata. For example, step 401 may include, for one or more patients,acquiring a digital representation (e.g., the memory or digital storage(e.g., hard drive, network drive) of a computational device such as acomputer, laptop, DSP, server, etc.) of an image scan of a patient, adigital representation including regions of interest, clinicalparameters, and a set of derived quantities calculated from the imagescan and the digital representation.

In one embodiment, the image scan of the patient may include theascending aorta and coronary artery tree. The type of scan may includecardiac computed tomography (CCTA), MRI, ultrasound etc. The digitalrepresentation may be based on the image scan of the patient.Furthermore, the digital representation may encompass regions ofinterest. For example, step 401 may include isolating the regions ofinterest and/or receiving the digital representation with regions ofinterest isolated. For instance, centerlines, which pass through thecenter of vessels of interest, may be computed. The computed centerlinesmay be used to construct lumen segments manually or automatically, andvoxels belonging to the aorta and to the lumen of the coronary arteriesmay be identified. Based on an identification of relevant voxels, ageometric model of the aorta and relevant coronary arteries may bereconstructed.

In addition to CCTA, the set of clinical parameters may be measured,where the parameters may include heart-rate, systolic and diastolicbrachial blood pressures, hematocrit, patient height and weight, andpatient history, e.g., smoking status, presence/absence of diabetes,etc. A set of derived quantities may be calculated from the image scanand the digital representation. These derived quantities may include:

Myocardial mass (M_(myo)), which may be obtained by image segmentationof the left ventricle. For instance, the segmentation may be used tocalculate the volume of myocardium, where multiplying the volume of themyocardium with a blood density may yield the myocardial mass.

Body surface area, which may be calculated from the patient height (h)and weight (w) as

${BSA} = {\sqrt{\frac{hw}{3600}}.}$

Viscosity, which may be calculated from the hematocrit (hem) as

$\eta = \frac{c}{\left( {1 - \frac{hem}{100}} \right)^{2.5}}$

where c is 0.0012.

Inlet aortic flow rate (Q), which may be calculated from scaling studiesas Q=1/60 BSA^(1.15)

Coronary flow rate (q_(cor)), which may be calculated from myocardialmass as

$q_{cor} = {c_{dil}\frac{5.09}{60}m_{myo}^{0.75}}$

where c_(dil) may be the dilation factor.

Coronary resistance, where the net coronary resistance may be calculatedfrom the desired coronary flow, and the value for individual outlets maybe calculated based on their areas.

Resistance of outlet aorta, which may be calculated based on aorticpressure, aortic flow rate, and desired coronary flow rate.

In one embodiment, step 403 may include calculating FFR_(CT) in arteriesof interest. For example, step 403 may include calculating FFR valuesfor each patient that underwent CCTA. For instance, FFR values may becalculated by solving Navier-Stokes equations. More specifically, step403 may include discretizing the arteries of interest into finiteelements, using the measured aortic pressure at the aortic inlet of thecomputational model and using resistances (e.g., coronary resistance(s)calculated in step 401) at all of the outlets. The resulting set ofequations after discretization may be solved to calculate blood velocityand pressure at all of the discretized nodes.

In one embodiment, step 405 may include solving an advection-diffusionequation. A general advection diffusion equation may include a set ofsources and a set of sinks, where solving the advection diffusionequation may involve solving for concentration in the rest of thedomain. For step 405, a basic embodiment may include using one source(e.g., a location where contrast is injected). Some embodiments mayinclude advection diffusion equations with multiple sources, forinstance, where contrast agent may be injected in the computationaldomain or geometry. In some cases, the computational model may notinclude injection location. In one embodiment, a two-phase systemcomprised of the virtual contrast agent and blood, may be given by theadvection diffusion equation:

$\frac{\partial c}{\partial t} = {{\nabla{.\left( {D{\nabla c}} \right)}} - {\nabla{.({vc})}}}$

where v may be the velocity of blood, c may be the concentration ofcontrast, and D may be the diffusivity of contrast agent in blood. Inone embodiment, step 405 may further include assigning boundaryconditions, modeling diffusivity, and then solving theadvection-diffusion equation based on the boundary conditions andmodeling. Regarding boundary conditions, the concentration of thecontrast agent at time t=0, across lumen at the ostia (o), may bec(x=ostium, t=0)=c₀. Since blood velocity at walls of the artery may bemodeled as zero, a gradient of concentration at vessel walls in adirection normal to the wall may be imposed to be zero. The sameboundary condition may be used at the truncated coronary boundaries.

Regarding modeling diffusivity, values for molecular diffusivity of, forexample, Gadolinium based contrast in blood has been reported inliterature. However, these values may change based on temperature,rheological properties of blood, etc. Hence, step 405 may includecalculating a patient-specific diffusivity by using a contrastconcentration directly from one or more CCTA scans, and solving aninverse problem to calculate diffusivity. The inverse problem mayinvolve solving the following optimization problem:

{tilde over (D)}=arg min_(D))(c(D)−c _(meas))²

where c_(meas) may be measured contrast concentration at proximallocations close to the ostium (e.g., locations picked so thatconcentration may be reliably inferred from intensity of images).

Regarding solving the advection-diffusion equations, advection-diffusionequations may be discretized in space using the same computational meshas used to solve Navier-Stokes equations (e.g., from step 403). A finitedifference scheme may be used to discretize the equations in time.Hence, by starting from the initial conditions and solving thediscretized equations sequentially, contrast concentration may becalculated throughout the arteries of interest.

In one embodiment, step 407 may include calculating CFD-derived TAGscores. For example, step 407 may include calculating transluminalgradients using a linear regression on the contrast concentration alonglesions. While these gradients may have one-to-one correspondence withTAG, the gradients may not be identical since TAG may use radiointensitymeasured in Hounsfield units. A scaling factor may be used to convertconcentration gradient into TAG scores. For example, TAG scores atspecific measurement locations (e.g., locations where contrastconcentration was measured for step 405) may be used as references forthe scaling factor.

In one embodiment, step 409 may include assigning a TAG threshold score.In one embodiment, a TAG threshold score may be computed by calculatingthe TAG coefficient on a database of patients with measured FFR andcalculating a TAG threshold score (T_(c)) having the best diagnosticaccuracy. For example, step 409 may employ the following equation:

{tilde over (T)} _(c)=arg min_(T) _(C) (I(TAG−T _(C))−I(FFR−0.8))²

In one embodiment, step 411 may include determining a hybrid scoreand/or providing a diagnosis. In one embodiment, step 411 may includeusing the calculated TAG score, and threshold TAG score may be used asstandalone metrics to assess risk of heart disease. Alternately, step411 may include using FFR_(CT) as a primary diagnostic tool. In such anembodiment, a calculated TAG score may be used if FFR_(CT) lies in anindeterminate region. In a further embodiment, TAG score and FFR_(CT),along with blood flow rate and geometric disease burden may be used asfeatures in a machine learning algorithm, where the algorithm may beused to calculate a regressor that maps these values to an hybridFFR_(CT-TAG) value. TAG and a profile of a contrast agent to improveupon a machine learning algorithm.

Various embodiments of the present disclosure relate generally toassessing risk of heart disease, specifically, using virtual contrastflow simulations to improve noninvasive metrics based on flow rate. Forexample, the present disclosure includes calculating TAG, CTFC, TMPG,and/or CCO to infer functional significance of a stenosis. Someinstances may include determining thresholds associated with themetrics, where calculations falling above or below the thresholdsindicate likelihood of heart disease. Furthermore, some instances mayinclude creating hybrid metrics to improve reliability of assessmentsoverall.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A system for processing patient-specific images todetermine blood flow, the system comprising: a data storage devicestoring instructions for processing patient-specific images to determineblood flow; and a processor configured to execute the instruction toperform a method including: receiving patient-specific image frames froman imaging system; generating a model of a vasculature system based onthe patient-specific image frames, and based on an observed contrastconcentration time associated with locations within the model usingcontrast dye in the vasculature system; determining a computed contrastconcentration time associated with the model of the vasculature systemusing computational fluid dynamics (CFD); adjusting the model of thevasculature system based on a comparison of the observed contrastconcentration time and the computed contrast concentration time; anddetermining a fractional flow reserve (FFR) based on the adjusted modelof the vasculature system.
 22. The system as recited in claim 21,wherein the patient-specific image frames include angiographic imagesfrom an x-ray system.
 23. The system as recited in claim 21, whereinadjusting the model of the vasculature system comprises adjustingboundary conditions.
 24. The system as recited in claim 21, whereinadjusting the model of the vascular system comprises dynamicallyadjusting a computational analysis of the model of the vascular systembased on the mismatch between the observed and computed contrastconcentration time.
 25. The system as recited in claim 21, wherein themodel of the vasculature is represented as a three-dimensional mesh. 26.The system as recited in claim 21, wherein the observed contrastconcentration time is measured using a frame count of a number of imageframes needed for the contrast dye to reach a landmark.
 27. The systemas recited in claim 21, wherein the computed contrast concentration timeis measured using frame count of a number of frames needed for thecontrast dye to attenuate to a predetermined degree or predeterminedconcentration.
 28. The system as recited in claim 21, wherein the FFRmeasurement is provided as a service in a cloud environment.
 29. Amethod for processing patient-specific images to determine blood flow,comprising: receiving patient-specific image frames from an imagingsystem; generating a model of a vasculature system based on thepatient-specific image frames, and based on an observed contrastconcentration time associated with locations within the model usingcontrast dye in the vasculature system; determining a computed contrastconcentration time associated with the model of the vasculature systemusing computational fluid dynamics (CFD); adjusting the model of thevasculature system based on a comparison of the observed contrastconcentration time and the computed contrast concentration time; anddetermining a fractional flow reserve (FFR) based on the adjusted modelof the vasculature system.
 30. The method of claim 29, wherein thepatient-specific image frames include angiographic images from an x-raysystem.
 31. The method of claim 29, wherein adjusting the model of thevasculature system comprises adjusting boundary conditions.
 32. Themethod of claim 29, wherein adjusting the model of the vascular systemcomprises dynamically adjusting a computational analysis of the model ofthe vascular system based on the mismatch between the observed andcomputed contrast concentration time.
 33. The method of claim 29,wherein the model of the vasculature is represented as athree-dimensional mesh.
 34. The method of claim 29, wherein the observedcontrast concentration time is measured using a frame count of a numberof image frames needed for the contrast dye to reach a landmark.
 35. Themethod of claim 29, wherein the computed contrast concentration time ismeasured using frame count of a number of frames needed for the contrastdye to attenuate to a predetermined degree or predeterminedconcentration.
 36. The method of claim 29, wherein the FFR measurementis provided as a service in a cloud environment.
 37. A non-transitorycomputer readable storage medium comprising a computer readable programfor processing patient-specific images to determine blood flow, whereinthe computer readable program when executed on a computer causes thecomputer to perform steps of: receiving patient-specific image framesfrom an imaging system; generating a model of a vasculature system basedon the patient-specific image frames, and based on an observed contrastconcentration time associated with locations within the model usingcontrast dye in the vasculature system; determining a computed contrastconcentration time associated with the model of the vasculature systemusing computational fluid dynamics (CFD); adjusting the model of thevasculature system based on a comparison of the observed contrastconcentration time and the computed contrast concentration time; anddetermining a fractional flow reserve (FFR) based on the adjusted modelof the vasculature system.
 38. The computer readable medium of claim 37,wherein adjusting the model of the vasculature system comprisesadjusting boundary conditions.
 39. The computer readable medium of claim37, wherein the observed contrast concentration time is measured using aframe count of a number of image frames needed for the contrast dye toreach a landmark.
 40. The computer readable medium of claim 37, whereinthe computed contrast concentration time is measured using frame countof a number of frames needed for the contrast dye to attenuate to apredetermined degree or predetermined concentration.